├── requirements.txt
├── README.md
├── __init__.py
├── utils.py
├── .devcontainer
    └── devcontainer.json
├── example_sklearn.py
└── Hello.py


/requirements.txt:
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1 | numpy
2 | pandas
3 | scikit-learn
4 | streamlit


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/README.md:
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1 | # Welcome to streamlit
2 | 
3 | This is the app you get when you run `streamlit hello`, extracted as its own app.
4 | 
5 | Edit [Hello.py](./Hello.py) to customize this app to your heart's desire. ❤️
6 | 
7 | Check it out on [Streamlit Community Cloud](https://st-hello-app.streamlit.app/)
8 | 


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/__init__.py:
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 1 | # Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)
 2 | #
 3 | # Licensed under the Apache License, Version 2.0 (the "License");
 4 | # you may not use this file except in compliance with the License.
 5 | # You may obtain a copy of the License at
 6 | #
 7 | #     http://www.apache.org/licenses/LICENSE-2.0
 8 | #
 9 | # Unless required by applicable law or agreed to in writing, software
10 | # distributed under the License is distributed on an "AS IS" BASIS,
11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 | # See the License for the specific language governing permissions and
13 | # limitations under the License.
14 | 


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/utils.py:
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 1 | # Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)
 2 | #
 3 | # Licensed under the Apache License, Version 2.0 (the "License");
 4 | # you may not use this file except in compliance with the License.
 5 | # You may obtain a copy of the License at
 6 | #
 7 | #     http://www.apache.org/licenses/LICENSE-2.0
 8 | #
 9 | # Unless required by applicable law or agreed to in writing, software
10 | # distributed under the License is distributed on an "AS IS" BASIS,
11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 | # See the License for the specific language governing permissions and
13 | # limitations under the License.
14 | 
15 | import inspect
16 | import textwrap
17 | 
18 | import streamlit as st
19 | 
20 | 
21 | def show_code(demo):
22 |     """Showing the code of the demo."""
23 |     show_code = st.sidebar.checkbox("Show code", True)
24 |     if show_code:
25 |         # Showing the code of the demo.
26 |         st.markdown("## Code")
27 |         sourcelines, _ = inspect.getsourcelines(demo)
28 |         st.code(textwrap.dedent("".join(sourcelines[1:])))
29 | 


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/.devcontainer/devcontainer.json:
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 1 | {
 2 |   "name": "Python 3",
 3 |   // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
 4 |   "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
 5 |   "customizations": {
 6 |     "codespaces": {
 7 |       "openFiles": [
 8 |         "README.md",
 9 |         "Hello.py"
10 |       ]
11 |     },
12 |     "vscode": {
13 |       "settings": {},
14 |       "extensions": [
15 |         "ms-python.python",
16 |         "ms-python.vscode-pylance"
17 |       ]
18 |     }
19 |   },
20 |   "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
21 |   "postAttachCommand": {
22 |     "server": "streamlit run Hello.py --server.enableCORS false --server.enableXsrfProtection false"
23 |   },
24 |   "portsAttributes": {
25 |     "8501": {
26 |       "label": "Application",
27 |       "onAutoForward": "openPreview"
28 |     }
29 |   },
30 |   "forwardPorts": [
31 |     8501
32 |   ]
33 | }
34 | 


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/example_sklearn.py:
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 1 | from time import time
 2 | 
 3 | import matplotlib.pyplot as plt
 4 | import numpy as np
 5 | 
 6 | from sklearn.cluster import KMeans
 7 | from sklearn.datasets import load_sample_image
 8 | from sklearn.metrics import pairwise_distances_argmin
 9 | from sklearn.utils import shuffle
10 | 
11 | n_colors = 64
12 | 
13 | # Load the Summer Palace photo
14 | china = load_sample_image("china.jpg")
15 | 
16 | # Convert to floats instead of the default 8 bits integer coding. Dividing by
17 | # 255 is important so that plt.imshow behaves works well on float data (need to
18 | # be in the range [0-1])
19 | china = np.array(china, dtype=np.float64) / 255
20 | 
21 | # Load Image and transform to a 2D numpy array.
22 | w, h, d = original_shape = tuple(china.shape)
23 | assert d == 3
24 | image_array = np.reshape(china, (w * h, d))
25 | 
26 | print("Fitting model on a small sub-sample of the data")
27 | t0 = time()
28 | image_array_sample = shuffle(image_array, random_state=0, n_samples=1_000)
29 | kmeans = KMeans(n_clusters=n_colors, n_init="auto", random_state=0).fit(
30 |     image_array_sample
31 | )
32 | print(f"done in {time() - t0:0.3f}s.")
33 | 
34 | # Get labels for all points
35 | print("Predicting color indices on the full image (k-means)")
36 | t0 = time()
37 | labels = kmeans.predict(image_array)
38 | print(f"done in {time() - t0:0.3f}s.")
39 | 
40 | 
41 | codebook_random = shuffle(image_array, random_state=0, n_samples=n_colors)
42 | print("Predicting color indices on the full image (random)")
43 | t0 = time()
44 | labels_random = pairwise_distances_argmin(codebook_random, image_array, axis=0)
45 | print(f"done in {time() - t0:0.3f}s.")
46 | 
47 | 
48 | def recreate_image(codebook, labels, w, h):
49 |     """Recreate the (compressed) image from the code book & labels"""
50 |     return codebook[labels].reshape(w, h, -1)
51 | 
52 | 
53 | # Display all results, alongside original image
54 | plt.figure(1)
55 | plt.clf()
56 | plt.axis("off")
57 | plt.title("Original image (96,615 colors)")
58 | plt.imshow(china)
59 | 
60 | plt.figure(2)
61 | plt.clf()
62 | plt.axis("off")
63 | plt.title(f"Quantized image ({n_colors} colors, K-Means)")
64 | plt.imshow(recreate_image(kmeans.cluster_centers_, labels, w, h))
65 | 
66 | plt.figure(3)
67 | plt.clf()
68 | plt.axis("off")
69 | plt.title(f"Quantized image ({n_colors} colors, Random)")
70 | plt.imshow(recreate_image(codebook_random, labels_random, w, h))
71 | plt.show()


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/Hello.py:
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 1 | from time import time
 2 | 
 3 | import streamlit as st
 4 | 
 5 | import numpy as np
 6 | 
 7 | from sklearn.datasets import load_sample_image
 8 | from sklearn.cluster import KMeans
 9 | from sklearn.utils import shuffle
10 | from sklearn.metrics import pairwise_distances_argmin
11 | 
12 | 
13 | def run():
14 |     st.set_page_config(
15 |         page_title="Hello",
16 |         page_icon="👋",
17 |     )
18 | 
19 |     st.write("# Welcome to :red[C]:gray[u]:green[l]:blue[ì] a color clustering app! 🎨")
20 | 
21 |     which_img = st.selectbox("Pick an image", options=["china.jpg", "flower.jpg"])
22 |     img = load_sample_image(which_img)
23 |     # st.write(img)
24 |     # st.image(img)
25 | 
26 |     # Convert to floats instead of the default 8 bits integer coding. Dividing by
27 |     # 255 is important so that plt.imshow behaves works well on float data (need to
28 |     # be in the range [0-1])
29 |     img_float = np.array(img, dtype=np.float64) / 255
30 |     st.image(img_float) # works the same as img!
31 | 
32 |     # Load Image and transform to a 2D numpy array.
33 |     w, h, d = original_shape = tuple(img_float.shape)
34 |     assert d == 3
35 |     img_array = np.reshape(img_float, (w * h, d))
36 | 
37 |     n_colors = st.number_input("How many colors?", min_value=2, max_value=256, value=8)
38 | 
39 |     st.write("Fitting model on a small sub-sample of the data")
40 |     t0 = time()
41 |     img_array_sample = shuffle(img_array, random_state=0, n_samples=1_000)
42 |     kmeans = KMeans(n_clusters=n_colors, n_init="auto", random_state=0).fit(
43 |         img_array_sample
44 |     )
45 |     st.write(f"done in {time() - t0:0.3f}s.")
46 | 
47 |     # Get labels for all points
48 |     st.write("Predicting color indices on the full image (k-means)")
49 |     t0 = time()
50 |     labels = kmeans.predict(img_array)
51 |     st.write(f"done in {time() - t0:0.3f}s.")
52 | 
53 |     st.write(f"### Quantized image ({n_colors} colors, K-Means)")
54 |     st.image(recreate_image(kmeans.cluster_centers_, labels, w, h))
55 | 
56 |     # random color selection
57 |     codebook_random = shuffle(img_array, random_state=0, n_samples=n_colors)
58 |     st.write("Predicting color indices on the full image (random)")
59 |     t0 = time()
60 |     labels_random = pairwise_distances_argmin(codebook_random, img_array, axis=0)
61 |     st.write(f"done in {time() - t0:0.3f}s.")
62 | 
63 |     st.write(f"### Quantized image ({n_colors} colors, Random)")
64 |     st.image(recreate_image(codebook_random, labels_random, w, h))
65 | 
66 | 
67 | def recreate_image(codebook, labels, w, h):
68 |     """Recreate the (compressed) image from the code book & labels"""
69 |     return codebook[labels].reshape(w, h, -1)
70 | 
71 | if __name__ == "__main__":
72 |     run()
73 | 


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